A library of discrete objectives

Bayesian adaptive calibration and optimal design (BACON) paper accepted at NeurIPS 2024

Benchmarks for Model-Based Optimization

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Gaussian processes in JAX.

The tiniest of Gaussian Process libraries

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

No Nonsense Neovim Client in Rust

Software to help decision-makers control the ethical and business impacts of their AI systems.

R and python implementations of Accelerated Bayesian Causal Forest.

Bayesian Causal Forests

Cross platform Neovim front-end UI, built with F# + Avalonia

Multiple dispatch in Python

Multiple argument dispatching.

Automated generation and evaluation of machine learning pipelines

SciPy style API for NLopt

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…

A garden for scikit-learn compatible trees

A Python package to assess and improve fairness of machine learning models.

Large-scale spatial inference with Tensorflow.

GUI for neovim, without any web bloat

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

👤 Multi-Armed Bandit Algorithms Library (MAB) 👮

gtk ui for neovim

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…

Companion webpage to the book "Mathematics For Machine Learning"

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.